Hs_Apr3

Human fetal trachea samples collected on Apr3. v3 chemistry.

library(Seurat)
library(dplyr)

ZipF<-list.files(path=".",pattern="*.gz",full.names = T,recursive = T)
ZipF

library(plyr)
library(R.utils)
ldply(.data=ZipF, .fun=gunzip)  #This just unzips locally

##### First I manually changed all featurres.tsv to genes.tsv. Otherwise Read10X (Seurat v2) would not recognize.
# Load data
file_10Xdir_Hs<-c("GA21wk_v3","GA23wk_v3")
names(file_10Xdir_Hs)<-c("GA21wk_v3","GA23wk_v3")
Hs_Apr3_v3.data <- Read10X(data.dir = file_10Xdir_Hs)

dim(Hs_Apr3_v3.data)

26577 genes for HG38-plus

38892 “cells”/barcodes as filtered by Cell Ranger

Hs_GA2123_Trachea_v3@raw.data@Dim

[1] 22084 38892

head(Hs_GA2123_Trachea_v3@cell.names)

Hs_GA2123_Trachea_v3@data@Dim

[1] 22084  9693

cell_name<-read.table(text=Hs_GA2123_Trachea_v3@cell.names,sep="_",colClasses = "character")
age<-cell_name[,1]
names(age)<-Hs_GA2123_Trachea_v3@cell.names

Hs_GA2123_Trachea_v3<-AddMetaData(object = Hs_GA2123_Trachea_v3, metadata = age, col.name = "age")

table(Hs_GA2123_Trachea_v3@meta.data$age)

GA21wk GA23wk 
  7623   2070 

ribo.genes <- grep(pattern = "^RP[SL][[:digit:]]", x = rownames(x = Hs_GA2123_Trachea_v3@data), value = TRUE)
percent.ribo <- Matrix::colSums(Hs_GA2123_Trachea_v3@raw.data[ribo.genes, ])/Matrix::colSums(Hs_GA2123_Trachea_v3@raw.data)
Hs_GA2123_Trachea_v3 <- AddMetaData(object = Hs_GA2123_Trachea_v3, metadata = percent.ribo, col.name = "percent.ribo")

Hs_GA2123_Trachea_v3 <- NormalizeData(object = Hs_GA2123_Trachea_v3)

Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

Hs_GA2123_Trachea_v3 <- ScaleData(object = Hs_GA2123_Trachea_v3)

Scaling data matrix

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Hs_GA2123_Trachea_v3 <- FindVariableGenes(object = Hs_GA2123_Trachea_v3, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)

Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

Hs_GA2123_Trachea_v3 <- RunPCA(object = Hs_GA2123_Trachea_v3, do.print = FALSE)
Hs_GA2123_Trachea_v3 <- ProjectPCA(object = Hs_GA2123_Trachea_v3, do.print = FALSE)

PCHeatmap(object = Hs_GA2123_Trachea_v3, pc.use = 1:10, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 25)

PCElbowPlot(object = Hs_GA2123_Trachea_v3)

n.pcs = 20
res.used <- 0.8
Hs_GA2123_Trachea_v3 <- FindClusters(object = Hs_GA2123_Trachea_v3, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)

Hs_GA2123_Trachea_v3 <- RunTSNE(object = Hs_GA2123_Trachea_v3, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2)

TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = F,group.by="age",pt.size = 0.1)

n.pcs = 20
res.used <- 1.0
Hs_GA2123_Trachea_v3 <- FindClusters(object = Hs_GA2123_Trachea_v3, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE,force.recalc=T)

Hs_GA2123_Trachea_v3 <- RunTSNE(object = Hs_GA2123_Trachea_v3, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)

TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = T,group.by="res.1")

Hs_GA2123_Trachea_v3<-SetAllIdent(Hs_GA2123_Trachea_v3,id="res.1")
GA2123wk_v3.res1.clust.markers <- FindAllMarkers(object = Hs_GA2123_Trachea_v3, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)

GA2123wk_v3.res1.clust.markers %>% group_by(cluster) %>% top_n(20, avg_logFC)

write.table(GA2123wk_v3.res1.clust.markers,"GA2123wk_v3.res1.markers.txt",sep="\t")

Hs_v3_res1_8_18<-FindMarkers(Hs_GA2123_Trachea_v3,ident.1=c(8),ident.2=c(18),only.pos = F)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~04s          
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   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 04s

Hs_v3_res1_8_18

Hs_v3_res1_2over1<-FindMarkers(Hs_GA2123_Trachea_v3,ident.1=c(2),ident.2=c(1),only.pos = T)

   |                                                  | 0 % ~calculating  
   |+                                                 | 2 % ~03s          
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   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 03s

Hs_v3_res1_2over1

Hs_v3_res1_21_20<-FindMarkers(Hs_GA2123_Trachea_v3,ident.1=c(21),ident.2=c(20),only.pos = T)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~03s          
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   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 03s

Hs_v3_res1_21_20

n.pcs = 20
res.used <- 1.2
Hs_GA2123_Trachea_v3 <- FindClusters(object = Hs_GA2123_Trachea_v3, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)

Build parameters exactly match those of already computed and stored SNN. To force recalculation, set force.recalc to TRUE.

Hs_GA2123_Trachea_v3 <- RunTSNE(object = Hs_GA2123_Trachea_v3, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)

TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = T,group.by="res.1.2")

n.pcs = 20
res.used <- 1.4
Hs_GA2123_Trachea_v3 <- FindClusters(object = Hs_GA2123_Trachea_v3, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)

Build parameters exactly match those of already computed and stored SNN. To force recalculation, set force.recalc to TRUE.

Hs_GA2123_Trachea_v3 <- RunTSNE(object = Hs_GA2123_Trachea_v3, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)

TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = T,group.by="res.1.4")

sum(is.na(Hs_GA2123_Trachea_v3@meta.data$doublet_score))

[1] 0

Hs_GA2123_Trachea_v3<-SetAllIdent(Hs_GA2123_Trachea_v3,id="age")
VlnPlot(object = Hs_GA2123_Trachea_v3, features.plot = c("doublet_score"), nCol = 1,group.by="res.1.4",point.size.use=0.3,ident.include = "GA21wk")

VlnPlot(object = Hs_GA2123_Trachea_v3, features.plot = c("SOX10","PHOX2A", "PHOX2B","CHGA","ASCL1","RET"), nCol = 1,group.by="res.1.4",point.size.use=0.3)

subset the Non-EPCAM cells:

Hs_GA2123_Trachea_v3 <- SetAllIdent(object = Hs_GA2123_Trachea_v3, id = "res.1.4")
Hs_GA2123_Trachea_v3_nonEpcam<-SubsetData(object=Hs_GA2123_Trachea_v3,ident.use=c(0:7,9:13,15,16,18,20:24))
table(Hs_GA2123_Trachea_v3_nonEpcam@meta.data$res.1.4)

  0   1  10  11  12  13  15  16  18   2  20  21  22  23  24   3   4   5   6   7   9 
937 862 405 401 370 351 316 290 248 825 189 121  90  88  65 550 481 476 461 447 414 

colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data)[colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data) == 'res.0.8'] <- 'orig.0.8'
colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data)[colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data) == 'res.1.4'] <- 'orig.1.4'
colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data)[colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data) == 'res.1.2'] <- 'orig.1.2'

Hs_GA2123_Trachea_v3_nonEpcam <- ScaleData(object = Hs_GA2123_Trachea_v3_nonEpcam)

Scaling data matrix

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Hs_GA2123_Trachea_v3_nonEpcam <- FindVariableGenes(object = Hs_GA2123_Trachea_v3_nonEpcam, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)

Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

run PCA on the set of genes

Hs_GA2123_Trachea_v3_nonEpcam <- RunPCA(object = Hs_GA2123_Trachea_v3_nonEpcam, do.print = FALSE)
#PCAPlot(Hs_GA2123_Trachea_v3_nonEpcam)

Hs_GA2123_Trachea_v3_nonEpcam <- ProjectPCA(object = Hs_GA2123_Trachea_v3_nonEpcam, do.print = F)

PCElbowPlot(object = Hs_GA2123_Trachea_v3_nonEpcam)

PCHeatmap(object = Hs_GA2123_Trachea_v3_nonEpcam, pc.use = 1:20, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 25)

n.pcs = 20
res.used <- 0.8
Hs_GA2123_Trachea_v3_nonEpcam <- FindClusters(object = Hs_GA2123_Trachea_v3_nonEpcam, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)

Build parameters exactly match those of already computed and stored SNN. To force recalculation, set force.recalc to TRUE.Clustering parameters for resolution 0.8 exactly match those of already computed. 
  To force recalculation, set force.recalc to TRUE.

Hs_GA2123_Trachea_v3_nonEpcam <- RunTSNE(object = Hs_GA2123_Trachea_v3_nonEpcam, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)

TSNEPlot(object = Hs_GA2123_Trachea_v3_nonEpcam, do.label = T,group.by="res.0.8")

table(Hs_GA2123_Trachea_v3_nonEpcam@meta.data$orig.1.4,Hs_GA2123_Trachea_v3_nonEpcam@meta.data$res.0.8)

    
       0   1  10  11  12  13  14  15  16  17   2   3   4   5   6   7   8   9
  0  917  10   0   0   0   1   1   0   0   0   0   4   4   0   0   0   0   0
  1   13 739  65  18   5  10   0   0   0   0   0  11   0   0   0   1   0   0
  10   2   0   0   1   0   0   0   0   0   0   0   0   1   0   0   0   0 401
  11   0   0   0   1   1   0   0   1   0   0 396   0   0   0   1   1   0   0
  12   0   9   0 313  47   0   0   0   0   0   0   0   0   0   0   1   0   0
  13  11   5   1   0   3   0   0   0   0   0   0   0 329   1   0   1   0   0
  15   0  22 274   0   3   0   0   0   0   0   0  16   0   0   0   1   0   0
  16   0   9   5   4 256   0   1   0   0   0   0   0   0   0  13   0   2   0
  18   1  17   1   0   0 227   0   1   0   0   0   0   0   0   0   0   0   1
  2   15  37  30   0   0   0   0   0   0   0   0 743   0   0   0   0   0   0
  20   0   0   0   0   0   0   0   0   0   0   0   0   4 184   0   0   1   0
  21   5   0   0   0   0   0 115   0   0   0   0   0   0   0   0   1   0   0
  22   0   0   0   0   0   0   0   1  85   0   0   0   0   0   4   0   0   0
  23   0   0   0   0   0   0   0  88   0   0   0   0   0   0   0   0   0   0
  24   0   0   0   0   0   0   0   0   0  65   0   0   0   0   0   0   0   0
  3    0   0   0   0   0   0   0   0   1   0   0   0   0   0 542   7   0   0
  4    5   3   1   0   0   0   0   0   0   0   0   4   0   3   0 463   1   1
  5    1   4   1   1   2   1   0   3   5   0   0   1   1   0   6   1 449   0
  6    0   0   0   0   0   0   0   0   0   0   0   0   4 457   0   0   0   0
  7   19   0   0   0   0   0   0   0   0   0   0   0 414  12   0   1   1   0
  9    0   0   0   0   0   0   0   0   0   0 414   0   0   0   0   0   0   0

library(ggalluvial)

ggplot(data=Hs_GA2123_Trachea_v3_nonEpcam@meta.data,aes(axis1=orig.1.4,axis2=res.0.8))+geom_alluvium(aes(fill=res.0.8))+geom_stratum(width = 1/12, fill = "black", color = "grey") +geom_label(stat = "stratum", label.strata = TRUE)+scale_x_discrete(limits = c("orig.1.4", "res.0.8"), expand = c(.05, .05))

Now subset the basal, ciliated, and secretory:

Hs_GA2123_Trachea_v3 <- SetAllIdent(object = Hs_GA2123_Trachea_v3, id = "res.1.4")
Hs_GA2123_Trachea_v3_sub1<-SubsetData(object=Hs_GA2123_Trachea_v3,ident.use=c(8,14,19))
table(Hs_GA2123_Trachea_v3_sub1@meta.data$res.1.4)

colnames(Hs_GA2123_Trachea_v3_sub1@meta.data)[colnames(Hs_GA2123_Trachea_v3_sub1@meta.data) == 'res.0.8'] <- 'orig.0.8'
colnames(Hs_GA2123_Trachea_v3_sub1@meta.data)[colnames(Hs_GA2123_Trachea_v3_sub1@meta.data) == 'res.1.4'] <- 'orig.1.4'
colnames(Hs_GA2123_Trachea_v3_sub1@meta.data)[colnames(Hs_GA2123_Trachea_v3_sub1@meta.data) == 'res.1.2'] <- 'orig.1.2'

Hs_GA2123_Trachea_v3_sub1 <- ScaleData(object = Hs_GA2123_Trachea_v3_sub1)

Scaling data matrix

  |                                                                                                                     
  |                                                                                                               |   0%
  |                                                                                                                     
  |===============================================================================================================| 100%

Hs_GA2123_Trachea_v3_sub1 <- FindVariableGenes(object = Hs_GA2123_Trachea_v3_sub1, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)

Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

run PCA on the set of genes

Hs_GA2123_Trachea_v3_sub1 <- RunPCA(object = Hs_GA2123_Trachea_v3_sub1, do.print = FALSE)
#PCAPlot(Hs_GA2123_Trachea_v3_sub1)

Hs_GA2123_Trachea_v3_sub1 <- ProjectPCA(object = Hs_GA2123_Trachea_v3_sub1, do.print = F)

PCElbowPlot(object = Hs_GA2123_Trachea_v3_sub1)

PCHeatmap(object = Hs_GA2123_Trachea_v3_sub1, pc.use = 1:12, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 25)

n.pcs = 16
res.used <- 0.8
Hs_GA2123_Trachea_v3_sub1 <- FindClusters(object = Hs_GA2123_Trachea_v3_sub1, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)

Hs_GA2123_Trachea_v3_sub1 <- RunTSNE(object = Hs_GA2123_Trachea_v3_sub1, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)

TSNEPlot(object = Hs_GA2123_Trachea_v3_sub1, do.label = T)

prop.table(table(Hs_GA2123_Trachea_v3_sub1@meta.data$age,Hs_GA2123_Trachea_v3_sub1@meta.data$res.0.8),1)

        
                  0          1          2          3          4          5          6          7
  GA21wk 0.18484848 0.20303030 0.08333333 0.16818182 0.19090909 0.06363636 0.06060606 0.04545455
  GA23wk 0.18333333 0.10000000 0.31111111 0.10833333 0.05277778 0.12500000 0.06388889 0.05555556

n.pcs = 16
res.used <- 1.2
Hs_GA2123_Trachea_v3_sub1 <- FindClusters(object = Hs_GA2123_Trachea_v3_sub1, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)

Build parameters exactly match those of already computed and stored SNN. To force recalculation, set force.recalc to TRUE.

Hs_GA2123_Trachea_v3_sub1 <- RunTSNE(object = Hs_GA2123_Trachea_v3_sub1, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)

Hs_v3_sub1_res1.2_c8over2_4<-FindMarkers(Hs_GA2123_Trachea_v3_sub1,ident.1=c(8),ident.2 = c(2,4),only.pos = TRUE)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~16s          
   |++                                                | 2 % ~10s          
   |++                                                | 4 % ~07s          
   |+++                                               | 5 % ~06s          
   |++++                                              | 6 % ~05s          
   |++++                                              | 8 % ~04s          
   |+++++                                             | 9 % ~04s          
   |+++++                                             | 10% ~04s          
   |++++++                                            | 11% ~03s          
   |+++++++                                           | 12% ~03s          
   |+++++++                                           | 14% ~03s          
   |++++++++                                          | 15% ~03s          
   |+++++++++                                         | 16% ~03s          
   |+++++++++                                         | 18% ~03s          
   |++++++++++                                        | 19% ~03s          
   |++++++++++                                        | 20% ~02s          
   |+++++++++++                                       | 21% ~02s          
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   |+++++++++++++                                     | 25% ~02s          
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   |++++++++++++++                                    | 28% ~02s          
   |+++++++++++++++                                   | 29% ~02s          
   |+++++++++++++++                                   | 30% ~02s          
   |++++++++++++++++                                  | 31% ~02s          
   |+++++++++++++++++                                 | 32% ~02s          
   |+++++++++++++++++                                 | 34% ~02s          
   |++++++++++++++++++                                | 35% ~02s          
   |+++++++++++++++++++                               | 36% ~02s          
   |+++++++++++++++++++                               | 38% ~02s          
   |++++++++++++++++++++                              | 39% ~02s          
   |++++++++++++++++++++                              | 40% ~02s          
   |+++++++++++++++++++++                             | 41% ~02s          
   |++++++++++++++++++++++                            | 42% ~02s          
   |++++++++++++++++++++++                            | 44% ~02s          
   |+++++++++++++++++++++++                           | 45% ~02s          
   |++++++++++++++++++++++++                          | 46% ~01s          
   |++++++++++++++++++++++++                          | 48% ~01s          
   |+++++++++++++++++++++++++                         | 49% ~01s          
   |+++++++++++++++++++++++++                         | 50% ~01s          
   |++++++++++++++++++++++++++                        | 51% ~01s          
   |+++++++++++++++++++++++++++                       | 52% ~01s          
   |+++++++++++++++++++++++++++                       | 54% ~01s          
   |++++++++++++++++++++++++++++                      | 55% ~01s          
   |+++++++++++++++++++++++++++++                     | 56% ~01s          
   |+++++++++++++++++++++++++++++                     | 58% ~01s          
   |++++++++++++++++++++++++++++++                    | 59% ~01s          
   |++++++++++++++++++++++++++++++                    | 60% ~01s          
   |+++++++++++++++++++++++++++++++                   | 61% ~01s          
   |++++++++++++++++++++++++++++++++                  | 62% ~01s          
   |++++++++++++++++++++++++++++++++                  | 64% ~01s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~01s          
   |++++++++++++++++++++++++++++++++++                | 66% ~01s          
   |++++++++++++++++++++++++++++++++++                | 68% ~01s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~01s          
   |+++++++++++++++++++++++++++++++++++               | 70% ~01s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~01s          
   |+++++++++++++++++++++++++++++++++++++             | 72% ~01s          
   |+++++++++++++++++++++++++++++++++++++             | 74% ~01s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~01s          
   |+++++++++++++++++++++++++++++++++++++++           | 76% ~01s          
   |+++++++++++++++++++++++++++++++++++++++           | 78% ~01s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
   |++++++++++++++++++++++++++++++++++++++++         | 80% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++        | 82% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++    | 90% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02s

Hs_v3_sub1_res1.2_c8over2_4

library(plyr)
Hs_GA2123_Trachea_v3_sub1@meta.data$cell_type<-mapvalues(Hs_GA2123_Trachea_v3_sub1@meta.data$res.1.2,from=c("0","1","2","3","4","5","6","7","8"),to=c("Secretory_SMG","Ciliated","Basal_SE","Epcam_ECM","Basal_SE","Myoepithelial","Ciliated_Foxn4","Secretory_SE","Basal_SMG"))

to annotate Hs_GA2123_Trachea_v3

Hs_v3_type_sub1<-Hs_GA2123_Trachea_v3_sub1@meta.data$cell_type
names(Hs_v3_type_sub1)<-Hs_GA2123_Trachea_v3_sub1@cell.names

Hs_GA2123_Trachea_v3@meta.data$cell_type<-mapvalues(Hs_GA2123_Trachea_v3@meta.data$res.1,from=c("0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22"),to=c("Fibroblast","Fibroblast","Fibroblast","VascularEndothelial","Fibroblast","Fibroblast","CyclingFibroblast","Fibroblast","Chondrocyte","Basal","Schwann/Neural","Fibroblast","Secretory","MesenchymalProgenitor","Stem","Fibroblast","Ciliated","Fibroblast","Chondrocyte","Immune","Muscle","Muscle","LymphaticEndothelial"))

Hs_GA2123_Trachea_v3<-AddMetaData(object = Hs_GA2123_Trachea_v3, metadata = Hs_v3_type_sub1, col.name = "specific_type")

table(Hs_GA2123_Trachea_v3@meta.data$specific_type)

      Basal_SE      Basal_SMG       Ciliated Ciliated_Foxn4      Epcam_ECM  Myoepithelial   Secretory_SE  Secretory_SMG 
           308             30            169             66            150             67             54            176 

Hs_GA2123_Trachea_v3@meta.data$specific_type <- ifelse(is.na(Hs_GA2123_Trachea_v3@meta.data$specific_type), as.character(Hs_GA2123_Trachea_v3@meta.data$cell_type), as.character(Hs_GA2123_Trachea_v3@meta.data$specific_type))

now we have annotation for all cells:

table(Hs_GA2123_Trachea_v3@meta.data$specific_type)

             Basal_SE             Basal_SMG           Chondrocyte              Ciliated        Ciliated_Foxn4     CyclingFibroblast 
                  308                    30                   650                   169                    66                   479 
            Epcam_ECM            Fibroblast                Immune  LymphaticEndothelial MesenchymalProgenitor                Muscle 
                  150                  5359                   121                    65                   316                   177 
        Myoepithelial        Schwann/Neural          Secretory_SE         Secretory_SMG                  Stem   VascularEndothelial 
                   67                   405                    54                   176                   286                   815 

print(levels(Hs_GA2123_Trachea_v3@ident))

 [1] "Basal_SE"              "Basal_SMG"             "Chondrocyte"           "Ciliated"              "Ciliated_Foxn4"       
 [6] "CyclingFibroblast"     "Epcam_ECM"             "Fibroblast"            "Immune"                "LymphaticEndothelial" 
[11] "MesenchymalProgenitor" "Muscle"                "Myoepithelial"         "Schwann/Neural"        "Secretory_SE"         
[16] "Secretory_SMG"         "Stem"                  "VascularEndothelial"  

Hs_GA2123_Trachea_v3<-SetAllIdent(object = Hs_GA2123_Trachea_v3, id = "specific_type")
Hs_GA2123_Trachea_v3@ident = factor(Hs_GA2123_Trachea_v3@ident,levels(Hs_GA2123_Trachea_v3@ident)[c(1,15,5,4,2,16,13,7,17,14,9,10,18,12,3,11,8,6)])

df_Hs<-FetchData(Hs_GA2123_Trachea_v3,c("ANO1","CFTR","SERPINB3","MUC16","specific_type"))

load(file="seurat_GA2123wk_v3.RData")